API - Utility

fit(sess, network, train_op, cost, X_train, ...) Traing a given non time-series network by the given cost function, training data, batch_size, n_epoch etc.
test(sess, network, acc, X_test, y_test, x, ...) Test a given non time-series network by the given test data and metric.
predict(sess, network, X, x, y_op[, batch_size]) Return the predict results of given non time-series network.
evaluation([y_test, y_predict, n_classes]) Input the predicted results, targets results and the number of class, return the confusion matrix, F1-score of each class, accuracy and macro F1-score.
class_balancing_oversample([X_train, ...]) Input the features and labels, return the features and labels after oversampling.
get_random_int([min, max, number, seed]) Return a list of random integer by the given range and quantity.
dict_to_one([dp_dict]) Input a dictionary, return a dictionary that all items are set to one, use for disable dropout, dropconnect layer and so on.
list_string_to_dict(string) Inputs ['a', 'b', 'c'], returns {'a': 0, 'b': 1, 'c': 2}.
flatten_list([list_of_list]) Input a list of list, return a list that all items are in a list.

Training, testing and predicting

Training

tensorlayer.utils.fit(sess, network, train_op, cost, X_train, y_train, x, y_, acc=None, batch_size=100, n_epoch=100, print_freq=5, X_val=None, y_val=None, eval_train=True, tensorboard=False, tensorboard_epoch_freq=5, tensorboard_weight_histograms=True, tensorboard_graph_vis=True)[source]

Traing a given non time-series network by the given cost function, training data, batch_size, n_epoch etc.

Parameters:

sess : TensorFlow session

sess = tf.InteractiveSession()

network : a TensorLayer layer

the network will be trained

train_op : a TensorFlow optimizer

like tf.train.AdamOptimizer

X_train : numpy array

the input of training data

y_train : numpy array

the target of training data

x : placeholder

for inputs

y_ : placeholder

for targets

acc : the TensorFlow expression of accuracy (or other metric) or None

if None, would not display the metric

batch_size : int

batch size for training and evaluating

n_epoch : int

the number of training epochs

print_freq : int

display the training information every print_freq epochs

X_val : numpy array or None

the input of validation data

y_val : numpy array or None

the target of validation data

eval_train : boolean

if X_val and y_val are not None, it refects whether to evaluate the training data

tensorboard : boolean

if True summary data will be stored to the log/ direcory for visualization with tensorboard. See also detailed tensorboard_X settings for specific configurations of features. (default False) Also runs tl.layers.initialize_global_variables(sess) internally in fit() to setup the summary nodes, see Note:

tensorboard_epoch_freq : int

how many epochs between storing tensorboard checkpoint for visualization to log/ directory (default 5)

tensorboard_weight_histograms : boolean

if True updates tensorboard data in the logs/ directory for visulaization of the weight histograms every tensorboard_epoch_freq epoch (default True)

tensorboard_graph_vis : boolean

if True stores the graph in the tensorboard summaries saved to log/ (default True)

Notes

If tensorboard=True, the global_variables_initializer will be run inside the fit function in order to initalize the automatically generated summary nodes used for tensorboard visualization, thus tf.global_variables_initializer().run() before the fit() call will be undefined.

Examples

>>> see tutorial_mnist_simple.py
>>> tl.utils.fit(sess, network, train_op, cost, X_train, y_train, x, y_,
...            acc=acc, batch_size=500, n_epoch=200, print_freq=5,
...            X_val=X_val, y_val=y_val, eval_train=False)
>>> tl.utils.fit(sess, network, train_op, cost, X_train, y_train, x, y_,
...            acc=acc, batch_size=500, n_epoch=200, print_freq=5,
...            X_val=X_val, y_val=y_val, eval_train=False,
...            tensorboard=True, tensorboard_weight_histograms=True, tensorboard_graph_vis=True)

Evaluation

tensorlayer.utils.test(sess, network, acc, X_test, y_test, x, y_, batch_size, cost=None)[source]

Test a given non time-series network by the given test data and metric.

Parameters:

sess : TensorFlow session

sess = tf.InteractiveSession()

network : a TensorLayer layer

the network will be trained

acc : the TensorFlow expression of accuracy (or other metric) or None

if None, would not display the metric

X_test : numpy array

the input of test data

y_test : numpy array

the target of test data

x : placeholder

for inputs

y_ : placeholder

for targets

batch_size : int or None

batch size for testing, when dataset is large, we should use minibatche for testing. when dataset is small, we can set it to None.

cost : the TensorFlow expression of cost or None

if None, would not display the cost

Examples

>>> see tutorial_mnist_simple.py
>>> tl.utils.test(sess, network, acc, X_test, y_test, x, y_, batch_size=None, cost=cost)

Prediction

tensorlayer.utils.predict(sess, network, X, x, y_op, batch_size=None)[source]

Return the predict results of given non time-series network.

Parameters:

sess : TensorFlow session

sess = tf.InteractiveSession()

network : a TensorLayer layer

the network will be trained

X : numpy array

the input

x : placeholder

for inputs

y_op : placeholder

the argmax expression of softmax outputs

batch_size : int or None

batch size for prediction, when dataset is large, we should use minibatche for prediction. when dataset is small, we can set it to None.

Examples

>>> see tutorial_mnist_simple.py
>>> y = network.outputs
>>> y_op = tf.argmax(tf.nn.softmax(y), 1)
>>> print(tl.utils.predict(sess, network, X_test, x, y_op))

Evaluation functions

tensorlayer.utils.evaluation(y_test=None, y_predict=None, n_classes=None)[source]

Input the predicted results, targets results and the number of class, return the confusion matrix, F1-score of each class, accuracy and macro F1-score.

Parameters:

y_test : numpy.array or list

target results

y_predict : numpy.array or list

predicted results

n_classes : int

number of classes

Examples

>>> c_mat, f1, acc, f1_macro = evaluation(y_test, y_predict, n_classes)

Class balancing functions

tensorlayer.utils.class_balancing_oversample(X_train=None, y_train=None, printable=True)[source]

Input the features and labels, return the features and labels after oversampling.

Parameters:

X_train : numpy.array

Features, each row is an example

y_train : numpy.array

Labels

Examples

  • One X
>>> X_train, y_train = class_balancing_oversample(X_train, y_train, printable=True)
  • Two X
>>> X, y = tl.utils.class_balancing_oversample(X_train=np.hstack((X1, X2)), y_train=y, printable=False)
>>> X1 = X[:, 0:5]
>>> X2 = X[:, 5:]

Random functions

tensorlayer.utils.get_random_int(min=0, max=10, number=5, seed=None)[source]

Return a list of random integer by the given range and quantity.

Examples

>>> r = get_random_int(min=0, max=10, number=5)
... [10, 2, 3, 3, 7]

Dictionary and list

Set all items in dictionary to one

tensorlayer.utils.dict_to_one(dp_dict={})[source]

Input a dictionary, return a dictionary that all items are set to one, use for disable dropout, dropconnect layer and so on.

Parameters:

dp_dict : dictionary

keeping probabilities

Examples

>>> dp_dict = dict_to_one( network.all_drop )
>>> dp_dict = dict_to_one( network.all_drop )
>>> feed_dict.update(dp_dict)

Convert list of string to dictionary

tensorlayer.utils.list_string_to_dict(string)[source]

Inputs ['a', 'b', 'c'], returns {'a': 0, 'b': 1, 'c': 2}.

Flatten a list

tensorlayer.utils.flatten_list(list_of_list=[[], []])[source]

Input a list of list, return a list that all items are in a list.

Parameters:list_of_list : a list of list

Examples

>>> tl.utils.flatten_list([[1, 2, 3],[4, 5],[6]])
... [1, 2, 3, 4, 5, 6]